Multi-resolution RiOMar data

!pip install xdggs
!pip install healpy
!pip install -U zarr==2.18.4
!pip install numcodecs==0.13.1
!pip install -U xarray

Requirement already satisfied: xdggs in /srv/conda/envs/notebook/lib/python3.12/site-packages (0.1.2.dev9+gb57dfb9)
Requirement already satisfied: xarray in /srv/conda/envs/notebook/lib/python3.12/site-packages (from xdggs) (2025.1.1)
Requirement already satisfied: cdshealpix in /srv/conda/envs/notebook/lib/python3.12/site-packages (from xdggs) (0.7.1)
Requirement already satisfied: h3ronpy in /srv/conda/envs/notebook/lib/python3.12/site-packages (from xdggs) (0.22.0)
Requirement already satisfied: typing-extensions in /srv/conda/envs/notebook/lib/python3.12/site-packages (from xdggs) (4.12.2)
Requirement already satisfied: lonboard>=0.9.3 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from xdggs) (0.9.3)
Requirement already satisfied: pyproj>=3.3 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from xdggs) (3.6.1)
Requirement already satisfied: matplotlib in /srv/conda/envs/notebook/lib/python3.12/site-packages (from xdggs) (3.9.2)
Requirement already satisfied: arro3-core>=0.4.0 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from xdggs) (0.4.5)
Requirement already satisfied: pooch in /srv/conda/envs/notebook/lib/python3.12/site-packages (from xdggs) (1.8.2)
Requirement already satisfied: anywidget<0.10.0,>=0.9.0 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from lonboard>=0.9.3->xdggs) (0.9.13)
Requirement already satisfied: geopandas>=0.13 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from lonboard>=0.9.3->xdggs) (1.0.1)
Requirement already satisfied: palettable<4.0.0,>=3.3.3 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from lonboard>=0.9.3->xdggs) (3.3.3)
Requirement already satisfied: pandas<3,>=2 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from lonboard>=0.9.3->xdggs) (2.2.2)
Requirement already satisfied: pyarrow>=14.0.1 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from lonboard>=0.9.3->xdggs) (17.0.0)
Requirement already satisfied: shapely<3,>=2 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from lonboard>=0.9.3->xdggs) (2.0.5)
Requirement already satisfied: contourpy>=1.0.1 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from matplotlib->xdggs) (1.2.1)
Requirement already satisfied: cycler>=0.10 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from matplotlib->xdggs) (0.12.1)
Requirement already satisfied: fonttools>=4.22.0 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from matplotlib->xdggs) (4.53.1)
Requirement already satisfied: kiwisolver>=1.3.1 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from matplotlib->xdggs) (1.4.5)
Requirement already satisfied: numpy>=1.23 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from matplotlib->xdggs) (1.26.4)
Requirement already satisfied: packaging>=20.0 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from matplotlib->xdggs) (24.1)
Requirement already satisfied: pillow>=8 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from matplotlib->xdggs) (10.4.0)
Requirement already satisfied: pyparsing>=2.3.1 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from matplotlib->xdggs) (3.1.2)
Requirement already satisfied: python-dateutil>=2.7 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from matplotlib->xdggs) (2.8.2)
Requirement already satisfied: certifi in /srv/conda/envs/notebook/lib/python3.12/site-packages (from pyproj>=3.3->xdggs) (2024.7.4)
Requirement already satisfied: astropy in /srv/conda/envs/notebook/lib/python3.12/site-packages (from cdshealpix->xdggs) (6.1.2)
Requirement already satisfied: platformdirs>=2.5.0 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from pooch->xdggs) (4.2.2)
Requirement already satisfied: requests>=2.19.0 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from pooch->xdggs) (2.32.3)
Requirement already satisfied: ipywidgets>=7.6.0 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from anywidget<0.10.0,>=0.9.0->lonboard>=0.9.3->xdggs) (8.1.3)
Requirement already satisfied: psygnal>=0.8.1 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from anywidget<0.10.0,>=0.9.0->lonboard>=0.9.3->xdggs) (0.11.1)
Requirement already satisfied: pyogrio>=0.7.2 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from geopandas>=0.13->lonboard>=0.9.3->xdggs) (0.9.0)
Requirement already satisfied: pytz>=2020.1 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from pandas<3,>=2->lonboard>=0.9.3->xdggs) (2024.1)
Requirement already satisfied: tzdata>=2022.7 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from pandas<3,>=2->lonboard>=0.9.3->xdggs) (2024.1)
Requirement already satisfied: six>=1.5 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from python-dateutil>=2.7->matplotlib->xdggs) (1.16.0)
Requirement already satisfied: charset-normalizer<4,>=2 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from requests>=2.19.0->pooch->xdggs) (3.3.2)
Requirement already satisfied: idna<4,>=2.5 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from requests>=2.19.0->pooch->xdggs) (3.7)
Requirement already satisfied: urllib3<3,>=1.21.1 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from requests>=2.19.0->pooch->xdggs) (1.26.19)
Requirement already satisfied: pyerfa>=2.0.1.1 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from astropy->cdshealpix->xdggs) (2.0.1.4)
Requirement already satisfied: astropy-iers-data>=0.2024.7.1.0.34.3 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from astropy->cdshealpix->xdggs) (0.2024.8.5.0.32.23)
Requirement already satisfied: PyYAML>=3.13 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from astropy->cdshealpix->xdggs) (6.0.2)
Requirement already satisfied: comm>=0.1.3 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from ipywidgets>=7.6.0->anywidget<0.10.0,>=0.9.0->lonboard>=0.9.3->xdggs) (0.2.2)
Requirement already satisfied: ipython>=6.1.0 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from ipywidgets>=7.6.0->anywidget<0.10.0,>=0.9.0->lonboard>=0.9.3->xdggs) (8.17.2)
Requirement already satisfied: traitlets>=4.3.1 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from ipywidgets>=7.6.0->anywidget<0.10.0,>=0.9.0->lonboard>=0.9.3->xdggs) (5.14.3)
Requirement already satisfied: widgetsnbextension~=4.0.11 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from ipywidgets>=7.6.0->anywidget<0.10.0,>=0.9.0->lonboard>=0.9.3->xdggs) (4.0.11)
Requirement already satisfied: jupyterlab-widgets~=3.0.11 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from ipywidgets>=7.6.0->anywidget<0.10.0,>=0.9.0->lonboard>=0.9.3->xdggs) (3.0.11)
Requirement already satisfied: decorator in /srv/conda/envs/notebook/lib/python3.12/site-packages (from ipython>=6.1.0->ipywidgets>=7.6.0->anywidget<0.10.0,>=0.9.0->lonboard>=0.9.3->xdggs) (5.1.1)
Requirement already satisfied: jedi>=0.16 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from ipython>=6.1.0->ipywidgets>=7.6.0->anywidget<0.10.0,>=0.9.0->lonboard>=0.9.3->xdggs) (0.19.1)
Requirement already satisfied: matplotlib-inline in /srv/conda/envs/notebook/lib/python3.12/site-packages (from ipython>=6.1.0->ipywidgets>=7.6.0->anywidget<0.10.0,>=0.9.0->lonboard>=0.9.3->xdggs) (0.1.7)
Requirement already satisfied: prompt-toolkit!=3.0.37,<3.1.0,>=3.0.30 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from ipython>=6.1.0->ipywidgets>=7.6.0->anywidget<0.10.0,>=0.9.0->lonboard>=0.9.3->xdggs) (3.0.38)
Requirement already satisfied: pygments>=2.4.0 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from ipython>=6.1.0->ipywidgets>=7.6.0->anywidget<0.10.0,>=0.9.0->lonboard>=0.9.3->xdggs) (2.18.0)
Requirement already satisfied: stack-data in /srv/conda/envs/notebook/lib/python3.12/site-packages (from ipython>=6.1.0->ipywidgets>=7.6.0->anywidget<0.10.0,>=0.9.0->lonboard>=0.9.3->xdggs) (0.6.2)
Requirement already satisfied: pexpect>4.3 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from ipython>=6.1.0->ipywidgets>=7.6.0->anywidget<0.10.0,>=0.9.0->lonboard>=0.9.3->xdggs) (4.9.0)
Requirement already satisfied: parso<0.9.0,>=0.8.3 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from jedi>=0.16->ipython>=6.1.0->ipywidgets>=7.6.0->anywidget<0.10.0,>=0.9.0->lonboard>=0.9.3->xdggs) (0.8.4)
Requirement already satisfied: ptyprocess>=0.5 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from pexpect>4.3->ipython>=6.1.0->ipywidgets>=7.6.0->anywidget<0.10.0,>=0.9.0->lonboard>=0.9.3->xdggs) (0.7.0)
Requirement already satisfied: wcwidth in /srv/conda/envs/notebook/lib/python3.12/site-packages (from prompt-toolkit!=3.0.37,<3.1.0,>=3.0.30->ipython>=6.1.0->ipywidgets>=7.6.0->anywidget<0.10.0,>=0.9.0->lonboard>=0.9.3->xdggs) (0.2.13)
Requirement already satisfied: executing>=1.2.0 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from stack-data->ipython>=6.1.0->ipywidgets>=7.6.0->anywidget<0.10.0,>=0.9.0->lonboard>=0.9.3->xdggs) (2.0.1)
Requirement already satisfied: asttokens>=2.1.0 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from stack-data->ipython>=6.1.0->ipywidgets>=7.6.0->anywidget<0.10.0,>=0.9.0->lonboard>=0.9.3->xdggs) (2.4.1)
Requirement already satisfied: pure-eval in /srv/conda/envs/notebook/lib/python3.12/site-packages (from stack-data->ipython>=6.1.0->ipywidgets>=7.6.0->anywidget<0.10.0,>=0.9.0->lonboard>=0.9.3->xdggs) (0.2.3)
Requirement already satisfied: healpy in /srv/conda/envs/notebook/lib/python3.12/site-packages (1.18.0)
Requirement already satisfied: numpy>=1.19 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from healpy) (1.26.4)
Requirement already satisfied: astropy in /srv/conda/envs/notebook/lib/python3.12/site-packages (from healpy) (6.1.2)
Requirement already satisfied: pyerfa>=2.0.1.1 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from astropy->healpy) (2.0.1.4)
Requirement already satisfied: astropy-iers-data>=0.2024.7.1.0.34.3 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from astropy->healpy) (0.2024.8.5.0.32.23)
Requirement already satisfied: PyYAML>=3.13 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from astropy->healpy) (6.0.2)
Requirement already satisfied: packaging>=19.0 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from astropy->healpy) (24.1)
Requirement already satisfied: zarr==2.18.4 in /srv/conda/envs/notebook/lib/python3.12/site-packages (2.18.4)
Requirement already satisfied: asciitree in /srv/conda/envs/notebook/lib/python3.12/site-packages (from zarr==2.18.4) (0.3.3)
Requirement already satisfied: numpy>=1.24 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from zarr==2.18.4) (1.26.4)
Requirement already satisfied: fasteners in /srv/conda/envs/notebook/lib/python3.12/site-packages (from zarr==2.18.4) (0.17.3)
Requirement already satisfied: numcodecs!=0.14.0,!=0.14.1,>=0.10.0 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from zarr==2.18.4) (0.13.1)
Requirement already satisfied: numcodecs==0.13.1 in /srv/conda/envs/notebook/lib/python3.12/site-packages (0.13.1)
Requirement already satisfied: numpy>=1.7 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from numcodecs==0.13.1) (1.26.4)
Requirement already satisfied: xarray in /srv/conda/envs/notebook/lib/python3.12/site-packages (2025.1.1)
Requirement already satisfied: numpy>=1.24 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from xarray) (1.26.4)
Requirement already satisfied: packaging>=23.2 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from xarray) (24.1)
Requirement already satisfied: pandas>=2.1 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from xarray) (2.2.2)
Requirement already satisfied: python-dateutil>=2.8.2 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from pandas>=2.1->xarray) (2.8.2)
Requirement already satisfied: pytz>=2020.1 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from pandas>=2.1->xarray) (2024.1)
Requirement already satisfied: tzdata>=2022.7 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from pandas>=2.1->xarray) (2024.1)
Requirement already satisfied: six>=1.5 in /srv/conda/envs/notebook/lib/python3.12/site-packages (from python-dateutil>=2.8.2->pandas>=2.1->xarray) (1.16.0)

import warnings
import healpy as hp
#import matplotlib.pyplot as plt
import numpy as np
import xarray as xr
import xdggs
#import fsspec

warnings.filterwarnings("ignore")
xr.set_options(display_max_rows=27)

<xarray.core.options.set_options at 0x7cc558f13530>

url_1 = "https://data-fair2adapt.ifremer.fr/riomar/small_healpix_14.zarr"
url_2 = "https://data-fair2adapt.ifremer.fr/riomar/small_healpix_13.zarr"
url_3 = "https://data-fair2adapt.ifremer.fr/riomar/small_healpix.zarr"

ds1 = xr.open_zarr(url_1).pipe(xdggs.decode)
ds1

<xarray.Dataset> Size: 6GB
Dimensions:       (cells: 3894803, s_rho: 40, time_counter: 5)
Coordinates:
  * cell_ids      (cells) uint64 31MB dask.array<chunksize=(121713,), meta=np.ndarray>
    latitude      (cells) float64 31MB dask.array<chunksize=(121713,), meta=np.ndarray>
    longitude     (cells) float64 31MB dask.array<chunksize=(121713,), meta=np.ndarray>
    resolution    float64 8B ...
  * s_rho         (s_rho) float32 160B -0.9875 -0.9625 ... -0.0375 -0.0125
  * time_counter  (time_counter) datetime64[ns] 40B 2004-01-01T00:58:30 ... 2...
    time_instant  (time_counter) datetime64[ns] 40B dask.array<chunksize=(1,), meta=np.ndarray>
Dimensions without coordinates: cells
Data variables:
    ocean_mask    (cells) float64 31MB dask.array<chunksize=(3894803,), meta=np.ndarray>
    temp          (time_counter, s_rho, cells) float64 6GB dask.array<chunksize=(1, 40, 3894803), meta=np.ndarray>
Indexes:
    cell_ids  HealpixIndex(level=14, indexing_scheme=nested)
Attributes:
    grid_type:  healpix
    lat:        0
    level:      14
    lon:        0
    nside:      16384
    rot_lat:    0
    rot_lon:    0

xarray.Dataset

• Dimensions:
  • cells: 3894803
  • s_rho: 40
  • time_counter: 5
• Coordinates: (7)
  • cell_ids
    (cells)
    uint64
    dask.array<chunksize=(121713,), meta=np.ndarray>

    grid_name :

    healpix

    level :

    14

    nest :

    True

    Array Chunk Bytes 29.71 MiB 0.93 MiB Shape (3894803,) (121713,) Dask graph 32 chunks in 2 graph layers Data type uint64 numpy.ndarray

  • latitude
    (cells)
    float64
    dask.array<chunksize=(121713,), meta=np.ndarray>

    Array Chunk Bytes 29.71 MiB 0.93 MiB Shape (3894803,) (121713,) Dask graph 32 chunks in 2 graph layers Data type float64 numpy.ndarray

  • longitude
    (cells)
    float64
    dask.array<chunksize=(121713,), meta=np.ndarray>

    Array Chunk Bytes 29.71 MiB 0.93 MiB Shape (3894803,) (121713,) Dask graph 32 chunks in 2 graph layers Data type float64 numpy.ndarray

  • resolution
    ()
    float64
    ...

    units :

    rad

    [1 values with dtype=float64]

  • s_rho
    (s_rho)
    float32
    -0.9875 -0.9625 ... -0.0375 -0.0125

    name :

    s_rho

    array([-0.9875, -0.9625, -0.9375, -0.9125, -0.8875, -0.8625, -0.8375, -0.8125,
           -0.7875, -0.7625, -0.7375, -0.7125, -0.6875, -0.6625, -0.6375, -0.6125,
           -0.5875, -0.5625, -0.5375, -0.5125, -0.4875, -0.4625, -0.4375, -0.4125,
           -0.3875, -0.3625, -0.3375, -0.3125, -0.2875, -0.2625, -0.2375, -0.2125,
           -0.1875, -0.1625, -0.1375, -0.1125, -0.0875, -0.0625, -0.0375, -0.0125],
          dtype=float32)

  • time_counter
    (time_counter)
    datetime64[ns]
    2004-01-01T00:58:30 ... 2004-01-...

    axis :

    T

    bounds :

    time_counter_bounds

    long_name :

    Time axis

    standard_name :

    time

    time_origin :

    2000-01-01 00:00:00

    array(['2004-01-01T00:58:30.000000000', '2004-01-01T01:58:30.000000000',
           '2004-01-01T02:58:30.000000000', '2004-01-01T03:58:30.000000000',
           '2004-01-01T04:58:30.000000000'], dtype='datetime64[ns]')

  • time_instant
    (time_counter)
    datetime64[ns]
    dask.array<chunksize=(1,), meta=np.ndarray>

    bounds :

    time_instant_bounds

    long_name :

    Time axis

    standard_name :

    time

    time_origin :

    2000-01-01 00:00:00

    Array Chunk Bytes 40 B 8 B Shape (5,) (1,) Dask graph 5 chunks in 2 graph layers Data type datetime64[ns] numpy.ndarray

• Data variables: (2)
  • ocean_mask
    (cells)
    float64
    dask.array<chunksize=(3894803,), meta=np.ndarray>

    Array Chunk Bytes 29.71 MiB 29.71 MiB Shape (3894803,) (3894803,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • temp
    (time_counter, s_rho, cells)
    float64
    dask.array<chunksize=(1, 40, 3894803), meta=np.ndarray>

    Array Chunk Bytes 5.80 GiB 1.16 GiB Shape (5, 40, 3894803) (1, 40, 3894803) Dask graph 5 chunks in 2 graph layers Data type float64 numpy.ndarray

• Indexes: (3)
  • s_rho
    PandasIndex

    PandasIndex(Index([   -0.987500011920929,   -0.9624999761581421,               -0.9375,
             -0.9125000238418579,    -0.887499988079071,    -0.862500011920929,
             -0.8374999761581421,               -0.8125,   -0.7875000238418579,
              -0.762499988079071,    -0.737500011920929,   -0.7124999761581421,
                         -0.6875,   -0.6625000238418579,    -0.637499988079071,
              -0.612500011920929,   -0.5874999761581421,               -0.5625,
             -0.5375000238418579,    -0.512499988079071,  -0.48750001192092896,
             -0.4625000059604645,               -0.4375,   -0.4124999940395355,
            -0.38749998807907104,  -0.36250001192092896,   -0.3375000059604645,
                         -0.3125,   -0.2874999940395355,  -0.26249998807907104,
            -0.23749999701976776,  -0.21250000596046448,               -0.1875,
            -0.16249999403953552,  -0.13750000298023224,  -0.11249999701976776,
            -0.08749999850988388,               -0.0625,  -0.03750000149011612,
           -0.012500000186264515],
          dtype='float32', name='s_rho'))

  • time_counter
    PandasIndex

    PandasIndex(DatetimeIndex(['2004-01-01 00:58:30', '2004-01-01 01:58:30',
                   '2004-01-01 02:58:30', '2004-01-01 03:58:30',
                   '2004-01-01 04:58:30'],
                  dtype='datetime64[ns]', name='time_counter', freq=None))

  • cell_ids
    HealpixIndex(level=14, indexing_scheme=nested)

    <xdggs.healpix.HealpixIndex object at 0x7cc4ec2274d0>

• Attributes: (7)

  grid_type :

  healpix

  lat :

  0

  level :

  14

  lon :

  0

  nside :

  16384

  rot_lat :

  0

  rot_lon :

  0

ds2 =  xr.open_zarr(url_2).pipe(xdggs.decode)
ds2

<xarray.Dataset> Size: 2GB
Dimensions:       (cells: 971899, s_rho: 40, time_counter: 5)
Coordinates:
  * cell_ids      (cells) uint64 8MB dask.array<chunksize=(60744,), meta=np.ndarray>
    latitude      (cells) float64 8MB dask.array<chunksize=(60744,), meta=np.ndarray>
    longitude     (cells) float64 8MB dask.array<chunksize=(60744,), meta=np.ndarray>
    resolution    float64 8B ...
  * s_rho         (s_rho) float32 160B -0.9875 -0.9625 ... -0.0375 -0.0125
  * time_counter  (time_counter) datetime64[ns] 40B 2004-01-01T00:58:30 ... 2...
    time_instant  (time_counter) datetime64[ns] 40B dask.array<chunksize=(1,), meta=np.ndarray>
Dimensions without coordinates: cells
Data variables:
    ocean_mask    (cells) float64 8MB dask.array<chunksize=(971899,), meta=np.ndarray>
    temp          (time_counter, s_rho, cells) float64 2GB dask.array<chunksize=(1, 40, 971899), meta=np.ndarray>
Indexes:
    cell_ids  HealpixIndex(level=13, indexing_scheme=nested)
Attributes:
    grid_type:  healpix
    lat:        0
    level:      13
    lon:        0
    nside:      8192
    rot_lat:    0
    rot_lon:    0

xarray.Dataset

• Dimensions:
  • cells: 971899
  • s_rho: 40
  • time_counter: 5
• Coordinates: (7)
  • cell_ids
    (cells)
    uint64
    dask.array<chunksize=(60744,), meta=np.ndarray>

    grid_name :

    healpix

    level :

    13

    nest :

    True

    Array Chunk Bytes 7.42 MiB 474.56 kiB Shape (971899,) (60744,) Dask graph 16 chunks in 2 graph layers Data type uint64 numpy.ndarray

  • latitude
    (cells)
    float64
    dask.array<chunksize=(60744,), meta=np.ndarray>

    Array Chunk Bytes 7.42 MiB 474.56 kiB Shape (971899,) (60744,) Dask graph 16 chunks in 2 graph layers Data type float64 numpy.ndarray

  • longitude
    (cells)
    float64
    dask.array<chunksize=(60744,), meta=np.ndarray>

    Array Chunk Bytes 7.42 MiB 474.56 kiB Shape (971899,) (60744,) Dask graph 16 chunks in 2 graph layers Data type float64 numpy.ndarray

  • resolution
    ()
    float64
    ...

    units :

    rad

    [1 values with dtype=float64]

  • s_rho
    (s_rho)
    float32
    -0.9875 -0.9625 ... -0.0375 -0.0125

    name :

    s_rho

    array([-0.9875, -0.9625, -0.9375, -0.9125, -0.8875, -0.8625, -0.8375, -0.8125,
           -0.7875, -0.7625, -0.7375, -0.7125, -0.6875, -0.6625, -0.6375, -0.6125,
           -0.5875, -0.5625, -0.5375, -0.5125, -0.4875, -0.4625, -0.4375, -0.4125,
           -0.3875, -0.3625, -0.3375, -0.3125, -0.2875, -0.2625, -0.2375, -0.2125,
           -0.1875, -0.1625, -0.1375, -0.1125, -0.0875, -0.0625, -0.0375, -0.0125],
          dtype=float32)

  • time_counter
    (time_counter)
    datetime64[ns]
    2004-01-01T00:58:30 ... 2004-01-...

    axis :

    T

    bounds :

    time_counter_bounds

    long_name :

    Time axis

    standard_name :

    time

    time_origin :

    2000-01-01 00:00:00

    array(['2004-01-01T00:58:30.000000000', '2004-01-01T01:58:30.000000000',
           '2004-01-01T02:58:30.000000000', '2004-01-01T03:58:30.000000000',
           '2004-01-01T04:58:30.000000000'], dtype='datetime64[ns]')

  • time_instant
    (time_counter)
    datetime64[ns]
    dask.array<chunksize=(1,), meta=np.ndarray>

    bounds :

    time_instant_bounds

    long_name :

    Time axis

    standard_name :

    time

    time_origin :

    2000-01-01 00:00:00

    Array Chunk Bytes 40 B 8 B Shape (5,) (1,) Dask graph 5 chunks in 2 graph layers Data type datetime64[ns] numpy.ndarray

• Data variables: (2)
  • ocean_mask
    (cells)
    float64
    dask.array<chunksize=(971899,), meta=np.ndarray>

    Array Chunk Bytes 7.42 MiB 7.42 MiB Shape (971899,) (971899,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • temp
    (time_counter, s_rho, cells)
    float64
    dask.array<chunksize=(1, 40, 971899), meta=np.ndarray>

    Array Chunk Bytes 1.45 GiB 296.60 MiB Shape (5, 40, 971899) (1, 40, 971899) Dask graph 5 chunks in 2 graph layers Data type float64 numpy.ndarray

• Indexes: (3)
  • s_rho
    PandasIndex

    PandasIndex(Index([   -0.987500011920929,   -0.9624999761581421,               -0.9375,
             -0.9125000238418579,    -0.887499988079071,    -0.862500011920929,
             -0.8374999761581421,               -0.8125,   -0.7875000238418579,
              -0.762499988079071,    -0.737500011920929,   -0.7124999761581421,
                         -0.6875,   -0.6625000238418579,    -0.637499988079071,
              -0.612500011920929,   -0.5874999761581421,               -0.5625,
             -0.5375000238418579,    -0.512499988079071,  -0.48750001192092896,
             -0.4625000059604645,               -0.4375,   -0.4124999940395355,
            -0.38749998807907104,  -0.36250001192092896,   -0.3375000059604645,
                         -0.3125,   -0.2874999940395355,  -0.26249998807907104,
            -0.23749999701976776,  -0.21250000596046448,               -0.1875,
            -0.16249999403953552,  -0.13750000298023224,  -0.11249999701976776,
            -0.08749999850988388,               -0.0625,  -0.03750000149011612,
           -0.012500000186264515],
          dtype='float32', name='s_rho'))

  • time_counter
    PandasIndex

    PandasIndex(DatetimeIndex(['2004-01-01 00:58:30', '2004-01-01 01:58:30',
                   '2004-01-01 02:58:30', '2004-01-01 03:58:30',
                   '2004-01-01 04:58:30'],
                  dtype='datetime64[ns]', name='time_counter', freq=None))

  • cell_ids
    HealpixIndex(level=13, indexing_scheme=nested)

    <xdggs.healpix.HealpixIndex object at 0x7cc4ec295a00>

• Attributes: (7)

  grid_type :

  healpix

  lat :

  0

  level :

  13

  lon :

  0

  nside :

  8192

  rot_lat :

  0

  rot_lon :

  0

ds3 =  xr.open_zarr(url_3).pipe(xdggs.decode)
ds3

<xarray.Dataset> Size: 395MB
Dimensions:       (cells: 242076, s_rho: 40, time_counter: 5)
Coordinates:
  * cell_ids      (cells) uint64 2MB dask.array<chunksize=(30260,), meta=np.ndarray>
    latitude      (cells) float64 2MB dask.array<chunksize=(30260,), meta=np.ndarray>
    longitude     (cells) float64 2MB dask.array<chunksize=(30260,), meta=np.ndarray>
    resolution    float64 8B ...
  * s_rho         (s_rho) float32 160B -0.9875 -0.9625 ... -0.0375 -0.0125
  * time_counter  (time_counter) datetime64[ns] 40B 2004-01-01T00:58:30 ... 2...
    time_instant  (time_counter) datetime64[ns] 40B dask.array<chunksize=(1,), meta=np.ndarray>
Dimensions without coordinates: cells
Data variables:
    ocean_mask    (cells) float64 2MB dask.array<chunksize=(242076,), meta=np.ndarray>
    temp          (time_counter, s_rho, cells) float64 387MB dask.array<chunksize=(1, 40, 242076), meta=np.ndarray>
Indexes:
    cell_ids  HealpixIndex(level=12, indexing_scheme=nested)
Attributes:
    grid_type:  healpix
    lat:        0
    level:      12
    lon:        0
    nside:      4096
    rot_lat:    0
    rot_lon:    0

xarray.Dataset

• Dimensions:
  • cells: 242076
  • s_rho: 40
  • time_counter: 5
• Coordinates: (7)
  • cell_ids
    (cells)
    uint64
    dask.array<chunksize=(30260,), meta=np.ndarray>

    grid_name :

    healpix

    level :

    12

    nest :

    True

    Array Chunk Bytes 1.85 MiB 236.41 kiB Shape (242076,) (30260,) Dask graph 8 chunks in 2 graph layers Data type uint64 numpy.ndarray

  • latitude
    (cells)
    float64
    dask.array<chunksize=(30260,), meta=np.ndarray>

    Array Chunk Bytes 1.85 MiB 236.41 kiB Shape (242076,) (30260,) Dask graph 8 chunks in 2 graph layers Data type float64 numpy.ndarray

  • longitude
    (cells)
    float64
    dask.array<chunksize=(30260,), meta=np.ndarray>

    Array Chunk Bytes 1.85 MiB 236.41 kiB Shape (242076,) (30260,) Dask graph 8 chunks in 2 graph layers Data type float64 numpy.ndarray

  • resolution
    ()
    float64
    ...

    units :

    rad

    [1 values with dtype=float64]

  • s_rho
    (s_rho)
    float32
    -0.9875 -0.9625 ... -0.0375 -0.0125

    name :

    s_rho

    array([-0.9875, -0.9625, -0.9375, -0.9125, -0.8875, -0.8625, -0.8375, -0.8125,
           -0.7875, -0.7625, -0.7375, -0.7125, -0.6875, -0.6625, -0.6375, -0.6125,
           -0.5875, -0.5625, -0.5375, -0.5125, -0.4875, -0.4625, -0.4375, -0.4125,
           -0.3875, -0.3625, -0.3375, -0.3125, -0.2875, -0.2625, -0.2375, -0.2125,
           -0.1875, -0.1625, -0.1375, -0.1125, -0.0875, -0.0625, -0.0375, -0.0125],
          dtype=float32)

  • time_counter
    (time_counter)
    datetime64[ns]
    2004-01-01T00:58:30 ... 2004-01-...

    axis :

    T

    bounds :

    time_counter_bounds

    long_name :

    Time axis

    standard_name :

    time

    time_origin :

    2000-01-01 00:00:00

    array(['2004-01-01T00:58:30.000000000', '2004-01-01T01:58:30.000000000',
           '2004-01-01T02:58:30.000000000', '2004-01-01T03:58:30.000000000',
           '2004-01-01T04:58:30.000000000'], dtype='datetime64[ns]')

  • time_instant
    (time_counter)
    datetime64[ns]
    dask.array<chunksize=(1,), meta=np.ndarray>

    bounds :

    time_instant_bounds

    long_name :

    Time axis

    standard_name :

    time

    time_origin :

    2000-01-01 00:00:00

    Array Chunk Bytes 40 B 8 B Shape (5,) (1,) Dask graph 5 chunks in 2 graph layers Data type datetime64[ns] numpy.ndarray

• Data variables: (2)
  • ocean_mask
    (cells)
    float64
    dask.array<chunksize=(242076,), meta=np.ndarray>

    Array Chunk Bytes 1.85 MiB 1.85 MiB Shape (242076,) (242076,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • temp
    (time_counter, s_rho, cells)
    float64
    dask.array<chunksize=(1, 40, 242076), meta=np.ndarray>

    Array Chunk Bytes 369.38 MiB 73.88 MiB Shape (5, 40, 242076) (1, 40, 242076) Dask graph 5 chunks in 2 graph layers Data type float64 numpy.ndarray

• Indexes: (3)
  • s_rho
    PandasIndex

    PandasIndex(Index([   -0.987500011920929,   -0.9624999761581421,               -0.9375,
             -0.9125000238418579,    -0.887499988079071,    -0.862500011920929,
             -0.8374999761581421,               -0.8125,   -0.7875000238418579,
              -0.762499988079071,    -0.737500011920929,   -0.7124999761581421,
                         -0.6875,   -0.6625000238418579,    -0.637499988079071,
              -0.612500011920929,   -0.5874999761581421,               -0.5625,
             -0.5375000238418579,    -0.512499988079071,  -0.48750001192092896,
             -0.4625000059604645,               -0.4375,   -0.4124999940395355,
            -0.38749998807907104,  -0.36250001192092896,   -0.3375000059604645,
                         -0.3125,   -0.2874999940395355,  -0.26249998807907104,
            -0.23749999701976776,  -0.21250000596046448,               -0.1875,
            -0.16249999403953552,  -0.13750000298023224,  -0.11249999701976776,
            -0.08749999850988388,               -0.0625,  -0.03750000149011612,
           -0.012500000186264515],
          dtype='float32', name='s_rho'))

  • time_counter
    PandasIndex

    PandasIndex(DatetimeIndex(['2004-01-01 00:58:30', '2004-01-01 01:58:30',
                   '2004-01-01 02:58:30', '2004-01-01 03:58:30',
                   '2004-01-01 04:58:30'],
                  dtype='datetime64[ns]', name='time_counter', freq=None))

  • cell_ids
    HealpixIndex(level=12, indexing_scheme=nested)

    <xdggs.healpix.HealpixIndex object at 0x7cc4ec2dd8b0>

• Attributes: (7)

  grid_type :

  healpix

  lat :

  0

  level :

  12

  lon :

  0

  nside :

  4096

  rot_lat :

  0

  rot_lon :

  0

Create the DataTree

dt = xr.DataTree.from_dict({"/" + str(ds1.attrs["level"]): ds1, "/" + str(ds2.attrs["level"]): ds2, "/" + str(ds3.attrs["level"]): ds3})  # create root node
dt

<xarray.DatasetView> Size: 0B
Dimensions:  ()
Data variables:
    *empty*

xarray.DataTree

• Groups: (3)
  <xarray.DatasetView> Size: 6GB
  Dimensions:       (cells: 3894803, s_rho: 40, time_counter: 5)
  Coordinates:
    * cell_ids      (cells) uint64 31MB dask.array<chunksize=(121713,), meta=np.ndarray>
      latitude      (cells) float64 31MB dask.array<chunksize=(121713,), meta=np.ndarray>
      longitude     (cells) float64 31MB dask.array<chunksize=(121713,), meta=np.ndarray>
      resolution    float64 8B ...
    * s_rho         (s_rho) float32 160B -0.9875 -0.9625 ... -0.0375 -0.0125
    * time_counter  (time_counter) datetime64[ns] 40B 2004-01-01T00:58:30 ... 2...
      time_instant  (time_counter) datetime64[ns] 40B dask.array<chunksize=(1,), meta=np.ndarray>
  Dimensions without coordinates: cells
  Data variables:
      ocean_mask    (cells) float64 31MB dask.array<chunksize=(3894803,), meta=np.ndarray>
      temp          (time_counter, s_rho, cells) float64 6GB dask.array<chunksize=(1, 40, 3894803), meta=np.ndarray>
  Indexes:
      cell_ids  HealpixIndex(level=14, indexing_scheme=nested)
  Attributes:
      grid_type:  healpix
      lat:        0
      level:      14
      lon:        0
      nside:      16384
      rot_lat:    0
      rot_lon:    0

  14

  • Groups: (0)
  • Dimensions:
    • cells: 3894803
    • s_rho: 40
    • time_counter: 5
  • Coordinates: (7)
    • cell_ids
      (cells)
      uint64
      dask.array<chunksize=(121713,), meta=np.ndarray>

      grid_name :

      healpix

      level :

      14

      nest :

      True

      Array Chunk Bytes 29.71 MiB 0.93 MiB Shape (3894803,) (121713,) Dask graph 32 chunks in 2 graph layers Data type uint64 numpy.ndarray

    • latitude
      (cells)
      float64
      dask.array<chunksize=(121713,), meta=np.ndarray>

      Array Chunk Bytes 29.71 MiB 0.93 MiB Shape (3894803,) (121713,) Dask graph 32 chunks in 2 graph layers Data type float64 numpy.ndarray

    • longitude
      (cells)
      float64
      dask.array<chunksize=(121713,), meta=np.ndarray>

      Array Chunk Bytes 29.71 MiB 0.93 MiB Shape (3894803,) (121713,) Dask graph 32 chunks in 2 graph layers Data type float64 numpy.ndarray

    • resolution
      ()
      float64
      ...

      units :

      rad

      [1 values with dtype=float64]

    • s_rho
      (s_rho)
      float32
      -0.9875 -0.9625 ... -0.0375 -0.0125

      name :

      s_rho

      array([-0.9875, -0.9625, -0.9375, -0.9125, -0.8875, -0.8625, -0.8375, -0.8125,
             -0.7875, -0.7625, -0.7375, -0.7125, -0.6875, -0.6625, -0.6375, -0.6125,
             -0.5875, -0.5625, -0.5375, -0.5125, -0.4875, -0.4625, -0.4375, -0.4125,
             -0.3875, -0.3625, -0.3375, -0.3125, -0.2875, -0.2625, -0.2375, -0.2125,
             -0.1875, -0.1625, -0.1375, -0.1125, -0.0875, -0.0625, -0.0375, -0.0125],
            dtype=float32)

    • time_counter
      (time_counter)
      datetime64[ns]
      2004-01-01T00:58:30 ... 2004-01-...

      axis :

      T

      bounds :

      time_counter_bounds

      long_name :

      Time axis

      standard_name :

      time

      time_origin :

      2000-01-01 00:00:00

      array(['2004-01-01T00:58:30.000000000', '2004-01-01T01:58:30.000000000',
             '2004-01-01T02:58:30.000000000', '2004-01-01T03:58:30.000000000',
             '2004-01-01T04:58:30.000000000'], dtype='datetime64[ns]')

    • time_instant
      (time_counter)
      datetime64[ns]
      dask.array<chunksize=(1,), meta=np.ndarray>

      bounds :

      time_instant_bounds

      long_name :

      Time axis

      standard_name :

      time

      time_origin :

      2000-01-01 00:00:00

      Array Chunk Bytes 40 B 8 B Shape (5,) (1,) Dask graph 5 chunks in 2 graph layers Data type datetime64[ns] numpy.ndarray

  • Data variables: (2)
    • ocean_mask
      (cells)
      float64
      dask.array<chunksize=(3894803,), meta=np.ndarray>

      Array Chunk Bytes 29.71 MiB 29.71 MiB Shape (3894803,) (3894803,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

    • temp
      (time_counter, s_rho, cells)
      float64
      dask.array<chunksize=(1, 40, 3894803), meta=np.ndarray>

      Array Chunk Bytes 5.80 GiB 1.16 GiB Shape (5, 40, 3894803) (1, 40, 3894803) Dask graph 5 chunks in 2 graph layers Data type float64 numpy.ndarray

  • Attributes: (7)

    grid_type :

    healpix

    lat :

    0

    level :

    14

    lon :

    0

    nside :

    16384

    rot_lat :

    0

    rot_lon :

    0

  <xarray.DatasetView> Size: 2GB
  Dimensions:       (cells: 971899, s_rho: 40, time_counter: 5)
  Coordinates:
    * cell_ids      (cells) uint64 8MB dask.array<chunksize=(60744,), meta=np.ndarray>
      latitude      (cells) float64 8MB dask.array<chunksize=(60744,), meta=np.ndarray>
      longitude     (cells) float64 8MB dask.array<chunksize=(60744,), meta=np.ndarray>
      resolution    float64 8B ...
    * s_rho         (s_rho) float32 160B -0.9875 -0.9625 ... -0.0375 -0.0125
    * time_counter  (time_counter) datetime64[ns] 40B 2004-01-01T00:58:30 ... 2...
      time_instant  (time_counter) datetime64[ns] 40B dask.array<chunksize=(1,), meta=np.ndarray>
  Dimensions without coordinates: cells
  Data variables:
      ocean_mask    (cells) float64 8MB dask.array<chunksize=(971899,), meta=np.ndarray>
      temp          (time_counter, s_rho, cells) float64 2GB dask.array<chunksize=(1, 40, 971899), meta=np.ndarray>
  Indexes:
      cell_ids  HealpixIndex(level=13, indexing_scheme=nested)
  Attributes:
      grid_type:  healpix
      lat:        0
      level:      13
      lon:        0
      nside:      8192
      rot_lat:    0
      rot_lon:    0

  13

  • Groups: (0)
  • Dimensions:
    • cells: 971899
    • s_rho: 40
    • time_counter: 5
  • Coordinates: (7)
    • cell_ids
      (cells)
      uint64
      dask.array<chunksize=(60744,), meta=np.ndarray>

      grid_name :

      healpix

      level :

      13

      nest :

      True

      Array Chunk Bytes 7.42 MiB 474.56 kiB Shape (971899,) (60744,) Dask graph 16 chunks in 2 graph layers Data type uint64 numpy.ndarray

    • latitude
      (cells)
      float64
      dask.array<chunksize=(60744,), meta=np.ndarray>

      Array Chunk Bytes 7.42 MiB 474.56 kiB Shape (971899,) (60744,) Dask graph 16 chunks in 2 graph layers Data type float64 numpy.ndarray

    • longitude
      (cells)
      float64
      dask.array<chunksize=(60744,), meta=np.ndarray>

      Array Chunk Bytes 7.42 MiB 474.56 kiB Shape (971899,) (60744,) Dask graph 16 chunks in 2 graph layers Data type float64 numpy.ndarray

    • resolution
      ()
      float64
      ...

      units :

      rad

      [1 values with dtype=float64]

    • s_rho
      (s_rho)
      float32
      -0.9875 -0.9625 ... -0.0375 -0.0125

      name :

      s_rho

      array([-0.9875, -0.9625, -0.9375, -0.9125, -0.8875, -0.8625, -0.8375, -0.8125,
             -0.7875, -0.7625, -0.7375, -0.7125, -0.6875, -0.6625, -0.6375, -0.6125,
             -0.5875, -0.5625, -0.5375, -0.5125, -0.4875, -0.4625, -0.4375, -0.4125,
             -0.3875, -0.3625, -0.3375, -0.3125, -0.2875, -0.2625, -0.2375, -0.2125,
             -0.1875, -0.1625, -0.1375, -0.1125, -0.0875, -0.0625, -0.0375, -0.0125],
            dtype=float32)

    • time_counter
      (time_counter)
      datetime64[ns]
      2004-01-01T00:58:30 ... 2004-01-...

      axis :

      T

      bounds :

      time_counter_bounds

      long_name :

      Time axis

      standard_name :

      time

      time_origin :

      2000-01-01 00:00:00

      array(['2004-01-01T00:58:30.000000000', '2004-01-01T01:58:30.000000000',
             '2004-01-01T02:58:30.000000000', '2004-01-01T03:58:30.000000000',
             '2004-01-01T04:58:30.000000000'], dtype='datetime64[ns]')

    • time_instant
      (time_counter)
      datetime64[ns]
      dask.array<chunksize=(1,), meta=np.ndarray>

      bounds :

      time_instant_bounds

      long_name :

      Time axis

      standard_name :

      time

      time_origin :

      2000-01-01 00:00:00

      Array Chunk Bytes 40 B 8 B Shape (5,) (1,) Dask graph 5 chunks in 2 graph layers Data type datetime64[ns] numpy.ndarray

  • Data variables: (2)
    • ocean_mask
      (cells)
      float64
      dask.array<chunksize=(971899,), meta=np.ndarray>

      Array Chunk Bytes 7.42 MiB 7.42 MiB Shape (971899,) (971899,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

    • temp
      (time_counter, s_rho, cells)
      float64
      dask.array<chunksize=(1, 40, 971899), meta=np.ndarray>

      Array Chunk Bytes 1.45 GiB 296.60 MiB Shape (5, 40, 971899) (1, 40, 971899) Dask graph 5 chunks in 2 graph layers Data type float64 numpy.ndarray

  • Attributes: (7)

    grid_type :

    healpix

    lat :

    0

    level :

    13

    lon :

    0

    nside :

    8192

    rot_lat :

    0

    rot_lon :

    0

  <xarray.DatasetView> Size: 395MB
  Dimensions:       (cells: 242076, s_rho: 40, time_counter: 5)
  Coordinates:
    * cell_ids      (cells) uint64 2MB dask.array<chunksize=(30260,), meta=np.ndarray>
      latitude      (cells) float64 2MB dask.array<chunksize=(30260,), meta=np.ndarray>
      longitude     (cells) float64 2MB dask.array<chunksize=(30260,), meta=np.ndarray>
      resolution    float64 8B ...
    * s_rho         (s_rho) float32 160B -0.9875 -0.9625 ... -0.0375 -0.0125
    * time_counter  (time_counter) datetime64[ns] 40B 2004-01-01T00:58:30 ... 2...
      time_instant  (time_counter) datetime64[ns] 40B dask.array<chunksize=(1,), meta=np.ndarray>
  Dimensions without coordinates: cells
  Data variables:
      ocean_mask    (cells) float64 2MB dask.array<chunksize=(242076,), meta=np.ndarray>
      temp          (time_counter, s_rho, cells) float64 387MB dask.array<chunksize=(1, 40, 242076), meta=np.ndarray>
  Indexes:
      cell_ids  HealpixIndex(level=12, indexing_scheme=nested)
  Attributes:
      grid_type:  healpix
      lat:        0
      level:      12
      lon:        0
      nside:      4096
      rot_lat:    0
      rot_lon:    0

  12

  • Groups: (0)
  • Dimensions:
    • cells: 242076
    • s_rho: 40
    • time_counter: 5
  • Coordinates: (7)
    • cell_ids
      (cells)
      uint64
      dask.array<chunksize=(30260,), meta=np.ndarray>

      grid_name :

      healpix

      level :

      12

      nest :

      True

      Array Chunk Bytes 1.85 MiB 236.41 kiB Shape (242076,) (30260,) Dask graph 8 chunks in 2 graph layers Data type uint64 numpy.ndarray

    • latitude
      (cells)
      float64
      dask.array<chunksize=(30260,), meta=np.ndarray>

      Array Chunk Bytes 1.85 MiB 236.41 kiB Shape (242076,) (30260,) Dask graph 8 chunks in 2 graph layers Data type float64 numpy.ndarray

    • longitude
      (cells)
      float64
      dask.array<chunksize=(30260,), meta=np.ndarray>

      Array Chunk Bytes 1.85 MiB 236.41 kiB Shape (242076,) (30260,) Dask graph 8 chunks in 2 graph layers Data type float64 numpy.ndarray

    • resolution
      ()
      float64
      ...

      units :

      rad

      [1 values with dtype=float64]

    • s_rho
      (s_rho)
      float32
      -0.9875 -0.9625 ... -0.0375 -0.0125

      name :

      s_rho

      array([-0.9875, -0.9625, -0.9375, -0.9125, -0.8875, -0.8625, -0.8375, -0.8125,
             -0.7875, -0.7625, -0.7375, -0.7125, -0.6875, -0.6625, -0.6375, -0.6125,
             -0.5875, -0.5625, -0.5375, -0.5125, -0.4875, -0.4625, -0.4375, -0.4125,
             -0.3875, -0.3625, -0.3375, -0.3125, -0.2875, -0.2625, -0.2375, -0.2125,
             -0.1875, -0.1625, -0.1375, -0.1125, -0.0875, -0.0625, -0.0375, -0.0125],
            dtype=float32)

    • time_counter
      (time_counter)
      datetime64[ns]
      2004-01-01T00:58:30 ... 2004-01-...

      axis :

      T

      bounds :

      time_counter_bounds

      long_name :

      Time axis

      standard_name :

      time

      time_origin :

      2000-01-01 00:00:00

      array(['2004-01-01T00:58:30.000000000', '2004-01-01T01:58:30.000000000',
             '2004-01-01T02:58:30.000000000', '2004-01-01T03:58:30.000000000',
             '2004-01-01T04:58:30.000000000'], dtype='datetime64[ns]')

    • time_instant
      (time_counter)
      datetime64[ns]
      dask.array<chunksize=(1,), meta=np.ndarray>

      bounds :

      time_instant_bounds

      long_name :

      Time axis

      standard_name :

      time

      time_origin :

      2000-01-01 00:00:00

      Array Chunk Bytes 40 B 8 B Shape (5,) (1,) Dask graph 5 chunks in 2 graph layers Data type datetime64[ns] numpy.ndarray

  • Data variables: (2)
    • ocean_mask
      (cells)
      float64
      dask.array<chunksize=(242076,), meta=np.ndarray>

      Array Chunk Bytes 1.85 MiB 1.85 MiB Shape (242076,) (242076,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

    • temp
      (time_counter, s_rho, cells)
      float64
      dask.array<chunksize=(1, 40, 242076), meta=np.ndarray>

      Array Chunk Bytes 369.38 MiB 73.88 MiB Shape (5, 40, 242076) (1, 40, 242076) Dask graph 5 chunks in 2 graph layers Data type float64 numpy.ndarray

  • Attributes: (7)

    grid_type :

    healpix

    lat :

    0

    level :

    12

    lon :

    0

    nside :

    4096

    rot_lat :

    0

    rot_lon :

    0

• Dimensions:
• Coordinates: (0)
• Inherited coordinates: (0)
• Data variables: (0)
• Attributes: (0)

Define functions

def create_arrow_table(polygons, arr, coords=None):
    from arro3.core import Array, ChunkedArray, Schema, Table

    if coords is None:
        coords = ["latitude", "longitude"]

    array = Array.from_arrow(polygons)
    name = arr.name or "data"
    arrow_arrays = {
        "geometry": array,
        "cell_ids": ChunkedArray([Array.from_numpy(arr.coords["cell_ids"])]),
        name: ChunkedArray([Array.from_numpy(arr.data)]),
    } | {
        coord: ChunkedArray([Array.from_numpy(arr.coords[coord].data)])
        for coord in coords
        if coord in arr.coords
    }

    fields = [array.field.with_name(name) for name, array in arrow_arrays.items()]
    schema = Schema(fields)

    return Table.from_arrays(list(arrow_arrays.values()), schema=schema)


def normalize(var, center=None):
    from matplotlib.colors import CenteredNorm, Normalize

    if center is None:
        vmin = var.min(skipna=True)
        vmax = var.max(skipna=True)
        normalizer = Normalize(vmin=vmin, vmax=vmax)
    else:
        halfrange = np.abs(var - center).max(skipna=True)
        normalizer = CenteredNorm(vcenter=center, halfrange=halfrange)

    return normalizer(var.data)


def explore_layer(
    arr,
    cell_dim="cells",
    cmap="viridis",
    center=None,
    alpha=None,
):
    from lonboard import SolidPolygonLayer
    from lonboard.colormap import apply_continuous_cmap
    from matplotlib import colormaps

    if len(arr.dims) != 1 or cell_dim not in arr.dims:
        raise ValueError(
            f"exploration only works with a single dimension ('{cell_dim}')"
        )

    cell_ids = arr.dggs.coord.data
    grid_info = arr.dggs.grid_info

    polygons = grid_info.cell_boundaries(cell_ids, backend="geoarrow")

    normalized_data = normalize(arr.variable, center=center)

    colormap = colormaps[cmap]
    colors = apply_continuous_cmap(normalized_data, colormap, alpha=alpha)

    table = create_arrow_table(polygons, arr)
    layer = SolidPolygonLayer(table=table, filled=True, get_fill_color=colors)

    return layer

import lonboard
ds_12 = dt["12"].ds
ds_12 = ds_12.isel(time_counter=0, s_rho=0).compute().dropna(dim='cells').pipe(xdggs.decode)
ds_12

<xarray.Dataset> Size: 6MB
Dimensions:       (cells: 156410)
Coordinates:
  * cell_ids      (cells) uint64 1MB 11451631 11451635 ... 57954657 57954658
    latitude      (cells) float64 1MB 49.25 49.25 49.26 ... 50.83 50.84 50.84
    longitude     (cells) float64 1MB 0.4251 0.4767 0.4768 ... -7.884 -7.91
    resolution    float64 8B 0.0002498
    s_rho         float32 4B -0.9875
    time_counter  datetime64[ns] 8B 2004-01-01T00:58:30
    time_instant  datetime64[ns] 8B 2004-01-01T00:58:30
Dimensions without coordinates: cells
Data variables:
    ocean_mask    (cells) float64 1MB 1.0 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0
    temp          (cells) float64 1MB 13.35 12.86 12.63 ... 8.407 7.52 7.486
Indexes:
    cell_ids  HealpixIndex(level=12, indexing_scheme=nested)
Attributes:
    grid_type:  healpix
    lat:        0
    level:      12
    lon:        0
    nside:      4096
    rot_lat:    0
    rot_lon:    0

xarray.Dataset

• Dimensions:
  • cells: 156410
• Coordinates: (7)
  • cell_ids
    (cells)
    uint64
    11451631 11451635 ... 57954658

    grid_name :

    healpix

    level :

    12

    nest :

    True

    array([11451631, 11451635, 11451638, ..., 57954656, 57954657, 57954658],
          dtype=uint64)

  • latitude
    (cells)
    float64
    49.25 49.25 49.26 ... 50.84 50.84

    array([49.25192685, 49.25192685, 49.26411015, ..., 50.83179309,
           50.84391549, 50.84391549])

  • longitude
    (cells)
    float64
    0.4251 0.4767 ... -7.884 -7.91

    array([ 0.42513599,  0.47666762,  0.47680412, ..., -7.90811775,
           -7.88370018, -7.91046996])

  • resolution
    ()
    float64
    0.0002498

    units :

    rad

    array(0.00024984)

  • s_rho
    ()
    float32
    -0.9875

    name :

    s_rho

    array(-0.9875, dtype=float32)

  • time_counter
    ()
    datetime64[ns]
    2004-01-01T00:58:30

    axis :

    T

    bounds :

    time_counter_bounds

    long_name :

    Time axis

    standard_name :

    time

    time_origin :

    2000-01-01 00:00:00

    array('2004-01-01T00:58:30.000000000', dtype='datetime64[ns]')

  • time_instant
    ()
    datetime64[ns]
    2004-01-01T00:58:30

    bounds :

    time_instant_bounds

    long_name :

    Time axis

    standard_name :

    time

    time_origin :

    2000-01-01 00:00:00

    array('2004-01-01T00:58:30.000000000', dtype='datetime64[ns]')

• Data variables: (2)
  • ocean_mask
    (cells)
    float64
    1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0

    array([1., 1., 1., ..., 1., 1., 1.])

  • temp
    (cells)
    float64
    13.35 12.86 12.63 ... 7.52 7.486

    array([13.34880072, 12.85711822, 12.62642792, ...,  8.40665577,
            7.5196235 ,  7.48556392])

• Indexes: (1)
  • cell_ids
    HealpixIndex(level=12, indexing_scheme=nested)

    <xdggs.healpix.HealpixIndex object at 0x7cc4ec2dd940>

• Attributes: (7)

  grid_type :

  healpix

  lat :

  0

  level :

  12

  lon :

  0

  nside :

  4096

  rot_lat :

  0

  rot_lon :

  0

ds_12.temp.dggs.explore(alpha=1)

import lonboard
ds_14 = dt["14"].ds
ds_14 = ds_14.isel(time_counter=0, s_rho=0).compute().dropna(dim='cells').pipe(xdggs.decode)
ds_14

<xarray.Dataset> Size: 100MB
Dimensions:       (cells: 2508887)
Coordinates:
  * cell_ids      (cells) uint64 20MB 183222103 183222107 ... 927275362
    latitude      (cells) float64 20MB 49.27 49.27 49.27 ... 50.86 50.86 50.86
    longitude     (cells) float64 20MB 0.09343 0.08055 0.08699 ... -7.971 -7.978
    resolution    float64 8B 6.246e-05
    s_rho         float32 4B -0.9875
    time_counter  datetime64[ns] 8B 2004-01-01T00:58:30
    time_instant  datetime64[ns] 8B 2004-01-01T00:58:30
Dimensions without coordinates: cells
Data variables:
    ocean_mask    (cells) float64 20MB 1.0 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0
    temp          (cells) float64 20MB 10.11 10.03 10.07 ... 6.252 5.662 6.062
Indexes:
    cell_ids  HealpixIndex(level=14, indexing_scheme=nested)
Attributes:
    grid_type:  healpix
    lat:        0
    level:      14
    lon:        0
    nside:      16384
    rot_lat:    0
    rot_lon:    0

xarray.Dataset

• Dimensions:
  • cells: 2508887
• Coordinates: (7)
  • cell_ids
    (cells)
    uint64
    183222103 183222107 ... 927275362

    grid_name :

    healpix

    level :

    14

    nest :

    True

    array([183222103, 183222107, 183222108, ..., 927275360, 927275361,
           927275362], dtype=uint64)

  • latitude
    (cells)
    float64
    49.27 49.27 49.27 ... 50.86 50.86

    array([49.2671559 , 49.2671559 , 49.2671559 , ..., 50.85906785,
           50.86209824, 50.86209824])

  • longitude
    (cells)
    float64
    0.09343 0.08055 ... -7.971 -7.978

    array([ 0.09343452,  0.080547  ,  0.08699076, ..., -7.97701406,
           -7.97091207, -7.9776075 ])

  • resolution
    ()
    float64
    6.246e-05

    units :

    rad

    array(6.24589055e-05)

  • s_rho
    ()
    float32
    -0.9875

    name :

    s_rho

    array(-0.9875, dtype=float32)

  • time_counter
    ()
    datetime64[ns]
    2004-01-01T00:58:30

    axis :

    T

    bounds :

    time_counter_bounds

    long_name :

    Time axis

    standard_name :

    time

    time_origin :

    2000-01-01 00:00:00

    array('2004-01-01T00:58:30.000000000', dtype='datetime64[ns]')

  • time_instant
    ()
    datetime64[ns]
    2004-01-01T00:58:30

    bounds :

    time_instant_bounds

    long_name :

    Time axis

    standard_name :

    time

    time_origin :

    2000-01-01 00:00:00

    array('2004-01-01T00:58:30.000000000', dtype='datetime64[ns]')

• Data variables: (2)
  • ocean_mask
    (cells)
    float64
    1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0

    array([1., 1., 1., ..., 1., 1., 1.])

  • temp
    (cells)
    float64
    10.11 10.03 10.07 ... 5.662 6.062

    array([10.10840442, 10.02933507, 10.06902678, ...,  6.25169623,
            5.66168711,  6.06198029])

• Indexes: (1)
  • cell_ids
    HealpixIndex(level=14, indexing_scheme=nested)

    <xdggs.healpix.HealpixIndex object at 0x7cc4d61b05c0>

• Attributes: (7)

  grid_type :

  healpix

  lat :

  0

  level :

  14

  lon :

  0

  nside :

  16384

  rot_lat :

  0

  rot_lon :

  0

ds_14.temp.dggs.explore(alpha=0.8)

# (ds_12.longitude > -7.96) & (ds_12.longitude <-4.9) &
ds_12_small = ds_12.where(  (ds_12.latitude <46.5))
ds_12_small

<xarray.Dataset> Size: 6MB
Dimensions:       (cells: 156410)
Coordinates:
  * cell_ids      (cells) uint64 1MB 11451631 11451635 ... 57954657 57954658
    latitude      (cells) float64 1MB 49.25 49.25 49.26 ... 50.83 50.84 50.84
    longitude     (cells) float64 1MB 0.4251 0.4767 0.4768 ... -7.884 -7.91
    resolution    float64 8B 0.0002498
    s_rho         float32 4B -0.9875
    time_counter  datetime64[ns] 8B 2004-01-01T00:58:30
    time_instant  datetime64[ns] 8B 2004-01-01T00:58:30
Dimensions without coordinates: cells
Data variables:
    ocean_mask    (cells) float64 1MB nan nan nan nan nan ... nan nan nan nan
    temp          (cells) float64 1MB nan nan nan nan nan ... nan nan nan nan
Indexes:
    cell_ids  HealpixIndex(level=12, indexing_scheme=nested)
Attributes:
    grid_type:  healpix
    lat:        0
    level:      12
    lon:        0
    nside:      4096
    rot_lat:    0
    rot_lon:    0

xarray.Dataset

• Dimensions:
  • cells: 156410
• Coordinates: (7)
  • cell_ids
    (cells)
    uint64
    11451631 11451635 ... 57954658

    grid_name :

    healpix

    level :

    12

    nest :

    True

    array([11451631, 11451635, 11451638, ..., 57954656, 57954657, 57954658],
          dtype=uint64)

  • latitude
    (cells)
    float64
    49.25 49.25 49.26 ... 50.84 50.84

    array([49.25192685, 49.25192685, 49.26411015, ..., 50.83179309,
           50.84391549, 50.84391549])

  • longitude
    (cells)
    float64
    0.4251 0.4767 ... -7.884 -7.91

    array([ 0.42513599,  0.47666762,  0.47680412, ..., -7.90811775,
           -7.88370018, -7.91046996])

  • resolution
    ()
    float64
    0.0002498

    units :

    rad

    array(0.00024984)

  • s_rho
    ()
    float32
    -0.9875

    name :

    s_rho

    array(-0.9875, dtype=float32)

  • time_counter
    ()
    datetime64[ns]
    2004-01-01T00:58:30

    axis :

    T

    bounds :

    time_counter_bounds

    long_name :

    Time axis

    standard_name :

    time

    time_origin :

    2000-01-01 00:00:00

    array('2004-01-01T00:58:30.000000000', dtype='datetime64[ns]')

  • time_instant
    ()
    datetime64[ns]
    2004-01-01T00:58:30

    bounds :

    time_instant_bounds

    long_name :

    Time axis

    standard_name :

    time

    time_origin :

    2000-01-01 00:00:00

    array('2004-01-01T00:58:30.000000000', dtype='datetime64[ns]')

• Data variables: (2)
  • ocean_mask
    (cells)
    float64
    nan nan nan nan ... nan nan nan nan

    array([nan, nan, nan, ..., nan, nan, nan])

  • temp
    (cells)
    float64
    nan nan nan nan ... nan nan nan nan

    array([nan, nan, nan, ..., nan, nan, nan])

• Indexes: (1)
  • cell_ids
    HealpixIndex(level=12, indexing_scheme=nested)

    <xdggs.healpix.HealpixIndex object at 0x7cc4e01cf2c0>

• Attributes: (7)

  grid_type :

  healpix

  lat :

  0

  level :

  12

  lon :

  0

  nside :

  4096

  rot_lat :

  0

  rot_lon :

  0

ds_12_small.temp.dggs.explore(alpha=0.8)

ds_14_small = ds_14.where((ds_14.latitude >=46.5) )
ds_14_small

<xarray.Dataset> Size: 100MB
Dimensions:       (cells: 2508887)
Coordinates:
  * cell_ids      (cells) uint64 20MB 183222103 183222107 ... 927275362
    latitude      (cells) float64 20MB 49.27 49.27 49.27 ... 50.86 50.86 50.86
    longitude     (cells) float64 20MB 0.09343 0.08055 0.08699 ... -7.971 -7.978
    resolution    float64 8B 6.246e-05
    s_rho         float32 4B -0.9875
    time_counter  datetime64[ns] 8B 2004-01-01T00:58:30
    time_instant  datetime64[ns] 8B 2004-01-01T00:58:30
Dimensions without coordinates: cells
Data variables:
    ocean_mask    (cells) float64 20MB 1.0 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0
    temp          (cells) float64 20MB 10.11 10.03 10.07 ... 6.252 5.662 6.062
Indexes:
    cell_ids  HealpixIndex(level=14, indexing_scheme=nested)
Attributes:
    grid_type:  healpix
    lat:        0
    level:      14
    lon:        0
    nside:      16384
    rot_lat:    0
    rot_lon:    0

xarray.Dataset

• Dimensions:
  • cells: 2508887
• Coordinates: (7)
  • cell_ids
    (cells)
    uint64
    183222103 183222107 ... 927275362

    grid_name :

    healpix

    level :

    14

    nest :

    True

    array([183222103, 183222107, 183222108, ..., 927275360, 927275361,
           927275362], dtype=uint64)

  • latitude
    (cells)
    float64
    49.27 49.27 49.27 ... 50.86 50.86

    array([49.2671559 , 49.2671559 , 49.2671559 , ..., 50.85906785,
           50.86209824, 50.86209824])

  • longitude
    (cells)
    float64
    0.09343 0.08055 ... -7.971 -7.978

    array([ 0.09343452,  0.080547  ,  0.08699076, ..., -7.97701406,
           -7.97091207, -7.9776075 ])

  • resolution
    ()
    float64
    6.246e-05

    units :

    rad

    array(6.24589055e-05)

  • s_rho
    ()
    float32
    -0.9875

    name :

    s_rho

    array(-0.9875, dtype=float32)

  • time_counter
    ()
    datetime64[ns]
    2004-01-01T00:58:30

    axis :

    T

    bounds :

    time_counter_bounds

    long_name :

    Time axis

    standard_name :

    time

    time_origin :

    2000-01-01 00:00:00

    array('2004-01-01T00:58:30.000000000', dtype='datetime64[ns]')

  • time_instant
    ()
    datetime64[ns]
    2004-01-01T00:58:30

    bounds :

    time_instant_bounds

    long_name :

    Time axis

    standard_name :

    time

    time_origin :

    2000-01-01 00:00:00

    array('2004-01-01T00:58:30.000000000', dtype='datetime64[ns]')

• Data variables: (2)
  • ocean_mask
    (cells)
    float64
    1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0

    array([1., 1., 1., ..., 1., 1., 1.])

  • temp
    (cells)
    float64
    10.11 10.03 10.07 ... 5.662 6.062

    array([10.10840442, 10.02933507, 10.06902678, ...,  6.25169623,
            5.66168711,  6.06198029])

• Indexes: (1)
  • cell_ids
    HealpixIndex(level=14, indexing_scheme=nested)

    <xdggs.healpix.HealpixIndex object at 0x7cc4d616b470>

• Attributes: (7)

  grid_type :

  healpix

  lat :

  0

  level :

  14

  lon :

  0

  nside :

  16384

  rot_lat :

  0

  rot_lon :

  0

ds_14_small.temp.dggs.explore(alpha=0.8)

lonboard.Map(
    [
        explore_layer(
            ds_12_small.temp,
            alpha=1,
        ),
        explore_layer(ds_14_small.temp, alpha=1),
    ]
)